A generalized design methodology for the output feedback regulation of a special type of systems with bounded inputs

In this work, a generalized design scheme for the output feedback regulation of a special type of systems with bounded inputs is proposed. It gives rise to a simple dynamic controller that guarantees the regulation objective avoiding input saturation, for any initial condition within a specific set that may comprehend the whole state space and that does not require any additional system data (apart from the output variable). Several processes, like double‐pipe heat exchangers, bioreactors, and binary distillation columns, are shown to be part of the type of systems that may be regulated through the developed methodology. The efficiency of the proposed scheme is corroborated through experimental and simulation results. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of states,faults and unknown disturbances in non-linear systems

In this paper, we extend existing theory on non-linear unknown input observer design to a wider class of non-linear systems where the systems are subject to unknown input and output disturbances and experience faults. The approach used is to decouple the faults and unknown disturbances from the rest of the system through a series of transformations on state and output equations. Once total disturbance decoupling is achieved, an appropriate observer for the disturbance free part of the non-linear system is proposed and designed. The designed observer is, subsequently, used for estimation of unknown inputs, outputs, and fault signals. Two examples are given for illustration.     

Hybrid dynamical systems with hybrid inputs: Definition of solutions and applications to interconnections

In this paper, we define solutions for hybrid systems with prespecified hybrid inputs. Unlike previous work where solutions and inputs are assumed to be defined on the same domain a priori, we consider the case where intervals of flow and jump times of the input are not necessarily synchronized with those of the state trajectory. This happens in particular when the input is the output of another hybrid system, for instance, in the context of observer design or reference tracking. The proposed approach relies on reparametrizing the jumps of the input in order to write it on a common domain. The solutions then consist of a pair made of the state trajectory and the reparametrized input. Our definition generalizes the notions of solutions of continuous‐time and discrete‐time systems with inputs. We provide an algorithm that automatically performs the construction of solutions for a given hybrid input. In the context of hybrid interconnections, we show how the solutions of the individual systems can be linked to the solutions of a closed‐loop system. Example illustrate the notions and the proposed algorithm.     

Persistent inputs and the standard H 2 multivariable control problem

The H ₂ control problem is formulated with exogenous inputs having unstable shape-deterministic components. Such inputs are called persistent. Some issues in connection with past solutions of the H ₂ control problem in this case are carefully described and addressed. In particular, it is established that two- and three-degree-of freedom (2DOF and 3DOF) systems with persistent inputs cannot be treated within the framework of the standard configuration. In addition, past treatments of persistent inputs within the framework of the generalised 2DOF configuration focused on solutions which yielded stable error transforms without explicitly requiring the same for controller outputs. In this article, a more general configuration is treated and physical considerations are invoked to justify imposition of the requirement that both the regulated variable z(s) and the controller output u(s) be stable. A persistent input model for which there exists a stabilising controller that makes both z(s) and u(s) stable is called acceptable and the necessary and sufficient condition for such acceptability is determined. Also considered is a persistent input model for which there exists a stabilising controller that makes the controller output, the measured output and the regulated variable stable. Such a model is called strictly acceptable and the necessary and sufficient condition for strict acceptability is given. The subset of all stabilising controllers associated with an acceptable persistent input model is parameterised and this parameterisation is used to formulate an H ₂ optimisation problem with persistent inputs which can then be solved using standard procedures.     

一种时变输入输出过程神经元网络及学习算法研究

针对输入／输出均为时变函数的非线性系统建模问题,提出一种时变输入输出过程神经元网络模型,并给出了具体的学习算法．过程神经元网络的输入、输出均可为时变函数,其空间、时间聚合算子分别取为空间加权求和及含时间变参积分,聚合运算和激励能同时反映时变输入信号的空间聚合作用和输入过程中的阶段时间累积效应．仿真实验结果验证了所提出模型和算法的有效性．     

On the global optimality of unbiased minimum-variance state estimation for systems with unknown inputs

In this paper, a globally optimal filtering framework is developed for unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system state and the output. The resulting optimal filters are globally optimal within the unbiased minimum-variance filtering over all linear unbiased estimators. Globally optimal state estimators with or without output and/or input transformations are derived. Through the global optimality evaluation of this research, the performance degradation of the filter proposed by Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867-876] is clearly illustrated and the global optimality of the filter proposed by Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934-937] is further verified. The relationship with the existing literature results is addressed. A unified approach to design a specific globally optimal state estimator that is based on the desired form of the distribution matrix from the unknown input to the output is also presented. A simulation example is given to illustrate the proposed results.     

A neural network to predict attendance of paediatric patients at outpatient clinics

The development of a feed-forward neural network to predict attendance and non-attendance at a hospital outpatient clinic is described. Particular emphasis is given to the post-processing applied to the network output, and to the choice of information presented to the network. The effect of individual inputs on the performance of the network is examined, and two methods of dealing with the problem of ambiguous input data are explored. It is shown that good results (approximately 90% accuracy) are achieved with a feedforward network — having 15 input nodes, a single hidden layer of five nodes and one output node with supervised backpropagation — even where data is limited, and that an understanding of the problem can lead to the modification of standard procedures and improved network performance.     

Left invertibility of discrete systems with finite inputs and quantised output

The aim of this article is to address left invertibility for dynamical systems with inputs and outputs in discrete sets. We study systems which evolve in discrete time within a continuous state-space. Quantised outputs are generated by the system according to a given partition of the state-space, while inputs are arbitrary sequences of symbols in a finite alphabet, which are associated to specific actions on the system. Our main results are obtained under some contractivity hypotheses. The problem of left invertibility, i.e. recovering an unknown input sequence from the knowledge of the corresponding output string, is addressed using the theory of iterated function systems (IFS), a tool developed for the study of fractals. We show how the IFS naturally associated to a system and the geometric properties of its attractor are linked to the invertibility property of the system. Our main result is a necessary and sufficient condition for left invertibility and uniform left invertibility for joint contractive systems. In addition, an algorithm is proposed to recover inputs from output strings. A few examples are presented to illustrate the application of the proposed method.     

Learning production systems from examples

This paper describes a program that learns production systems. The user presents examples consisting of inputs together with the required program output for each. If the productions in their current form would result in the output specified by the user for each input, no learning is needed. Otherwise the program must modify the production system so that it produces the correct output. The productions take arbitrary strings of LISP atoms as input and produce characterizations of them as output. Such a characterization can be as simple as the name of a concept to which the string belongs, but may involve the assertion of many properties and relations describing the input.     

Global trajectory tracking through output feedback for robot manipulators with bounded inputs

In this work, a globally stabilizing output feedback scheme for the trajectory tracking of robot manipulators with bounded inputs is proposed. It achieves the motion control objective avoiding input saturation and excluding velocity measurements. Moreover, it is not defined using a specific sigmoidal function, but any one on a set of saturation functions. Consequently, the proposed scheme actually constitutes a family of globally stabilizing output feedback bounded controllers. Furthermore, the control gains are not tied to satisfy any saturation‐avoidance inequality and may consequently take any positive value, which may be considered beneficial for performance adjustment/improvement purposes. Further, a class of desired trajectories that may be globally tracked avoiding input saturation and excluding velocity measurements is completely characterized. Global asymptotic stabilization of the closed‐loop system solutions towards the pre‐specified desired trajectory is proved through a strict Lyapunov function. The efficiency of the proposed scheme is corroborated through experimental results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于关键输入和多输入层高维小波网络

提出一种基于关键输入和加工工序的多输入层高维小波神经网络结构,该网络结构 是在传统前馈神经网络的基础上,将一部分输入节点根据实际情况移到神经网络的相关隐 层,关键输入节点不仅与随后一层隐节点相连,而且与输出节点相连,更真实地反映了大工 业生产过程中变量之间复杂的函数关系.将该种小波网络模型应用于连铸连轧生产线产品质 量建模,其效果较其他4种神经网络为优越.     

High order sliding mode observer for linear systems with unbounded unknown inputs

A global observer is designed for strongly detectable systems with unbounded unknown inputs. The design of the observer is based on three steps. First, the system is extended taking the unknown inputs (and possibly some of their derivatives) as a new state; then, using a global high-order sliding mode differentiator, a new output of the system is generated in order to fulfil, what we will call, the Hautus condition, which finally allows decomposing the system, in new coordinates, into two subsystems; the first one being unaffected directly by the unknown inputs, and the state vector of the second subsystem is obtained directly from the original system output. Such decomposition permits designing of a Luenberger observer for the first subsystem, which satisfies the Hautus condition, i.e. all the outputs have relative degree one w.r.t. the unknown inputs. This procedure enables one to estimate the state and the unknown inputs using the least number of differentiations possible. Simulations are given in order to show the effectiveness of the proposed observer.     

Synchronization of the neural response to noisy periodic synaptic input.

The timing information contained in the response of a neuron to noisy periodic synaptic input is analyzed for the leaky integrate-and-fire neural model. We address the question of the relationship between the timing of the synaptic inputs and the output spikes. This requires an analysis of the interspike interval distribution of the output spikes, which is obtained in the gaussian approximation. The conditional output spike density in response to noisy periodic input is evaluated as a function of the initial phase of the inputs. This enables the phase transition matrix to be calculated, which relates the phase at which the output spike is generated to the initial phase of the inputs. The interspike interval histogram and the period histogram for the neural response to ongoing periodic input are then evaluated by using the leading eigenvector of this phase transition matrix. The synchronization index of the output spikes is found to increase sharply as the inputs become synchronized. This enhancement of synchronization is most pronounced for large numbers of inputs and lower frequencies of modulation and also for rates of input near the critical input rate. However, the mutual information between the input phase of the stimulus and the timing of output spikes is found to decrease at low input rates as the number of inputs increases. The results show close agreement with those obtained from numerical simulations for large numbers of inputs.     

A framework for globally optimal state estimation for systems with unknown inputs (I): transformation approach

In this paper, a globally optimal state estimation is addressed in light of the conventional Luenberger observer‐type filter. This paper is the first part of a comprehensive extension of an original work by Hsieh, with the main aim being to develop a transformation‐based filtering framework for global unbiased minimum‐variance state estimation (GUMVSE) for systems with unknown inputs that affect both the system and the output. The main contributions of this paper are (i) a complete optimal solution for the GUMVSE is addressed, where both the globally optimal state filter and predictor are presented, and (ii) additional insights for implementing the globally optimal state filter are highlighted via the proposed decorrelation constraint. Compared with existing results, the proposed globally optimal filter has the most general filter form among all transformation‐based globally optimal filters in the sense that it does not use any specific unknown input transformation matrix in the derivation. A simulation example is given to illustrate the usefulness of the proposed results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Generalization of I-O-W model for waste management policy analysis and national planning

The generalization of the input-output-waste (I-O-W) model for different types of resources is presented in this paper as a new paradigm of national consistency modelling. This incorporates waste management (WM) policy analysis in a national planning context. The I-O-W model is an extension of the input-output model that formulates the waste aspects explicitly. The systematic analysis of intersectoral flow of different resources and wastes is important at the macro level for preparing resource plans that can be further integrated to frame overall economic plans. The paper briefly overviews the I-O-W model and generalizes it for five basic resources, i.e. material, energy, manpower, capital and services. The resource balance has been established by treating input to be equal to output plus waste for each resource independently as well as for the overall flow. The inputs required and the possible outputs for the generalized I-O-W model are listed. An illustrative example has been given that works out the direct, indirect and total energy. flows for a hypothetical economy. The scheme of integration is presented for overall WM policy analysis and national planning.     

A zero-base budgeting process with goal programming feedback

This paper describes the decision process used to establish the annual budget for allocating general fund revenues to non-revenue generating service activities of a city. This process combines zero-base budgeting (ZBB) and goal programming. The goal programming model formalizes the relationship between the ZBB decision packages specified by city departmental needs and the planning issue goals identified by either the city council, the city administrative staff or a citizens' survey. A budget committee reviews input data, forms the model and uses its output to revise budget inputs. The goal programming model provides a format for (1) effectively evaluating budget assumptions, (2) consistently representing the relationships between the ZBB decision packages and the city planning issues, and (3) quantifying and evaluating the contribution made by the decision packages toward satisfying the planning issue goals.     

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting

The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.     

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting

The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.     

Finite-time observer-based output-feedback control for the global stabilisation of the PVTOL aircraft with bounded inputs

In this work, an output-feedback scheme for the global stabilisation of the planar vertical take-off and landing aircraft with bounded inputs is developed taking into account the positive nature of the thrust. The global stabilisation objective is proven to be achieved avoiding input saturation and by exclusively considering the system positions in the feedback. To cope with the lack of velocity measurements, the proposed algorithm involves a finite-time observer. The generalised versions of the involved finite-time stabilisers have not only permitted to solve the output-feedback stabilisation problem avoiding input saturation, but also provide additional flexibility in the control design that may be used in aid of performance improvements. With respect to previous approaches, the developed finite-time observer-based scheme guarantees the global stabilisation objective disregarding velocity measurements in a bounded input context. Simulation tests corroborate the analytical developments. The study includes further experimental results on an actual flying device.